CI checks that can be automatically repaired (#119)

* fix isort & black & toml-sort & sphinx error * fix ci error * fix ci error * add comments * Update Makefile * change sphinx build command * add auto-lint * add black args * format with black * Auto Linting document * fix ci error --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: Young <afe.young@gmail.com>
2026-07-29 00:17:44 +00:00 · 2024-07-26 12:12:16 +08:00
parent 45b7a169fe
commit c7cfd397ca
56 changed files with 604 additions and 475 deletions
@@ -356,7 +356,9 @@ class FactorValueEvaluator(FactorEvaluator):

        # Check if both dataframe have the same rows count
        if gt_implementation is not None:
-            feedback_str, single_column_result = FactorRowCountEvaluator(self.scen).evaluate(implementation, gt_implementation)
+            feedback_str, single_column_result = FactorRowCountEvaluator(self.scen).evaluate(
+                implementation, gt_implementation
+            )
            conclusions.append(feedback_str)

            feedback_str, same_index_result = FactorIndexEvaluator(self.scen).evaluate(
@@ -364,7 +366,9 @@ class FactorValueEvaluator(FactorEvaluator):
            )
            conclusions.append(feedback_str)

-            feedback_str, output_format_result = FactorMissingValuesEvaluator(self.scen).evaluate(implementation, gt_implementation)
+            feedback_str, output_format_result = FactorMissingValuesEvaluator(self.scen).evaluate(
+                implementation, gt_implementation
+            )
            conclusions.append(feedback_str)

            feedback_str, equal_value_ratio_result = FactorEqualValueCountEvaluator(self.scen).evaluate(
@@ -464,8 +468,10 @@ class FactorFinalDecisionEvaluator(Evaluator):
            except KeyError as e:
                attempts += 1
                if attempts >= max_attempts:
-                    raise KeyError("Response from API is missing 'final_decision' or 'final_feedback' key after multiple attempts.") from e
-        
+                    raise KeyError(
+                        "Response from API is missing 'final_decision' or 'final_feedback' key after multiple attempts."
+                    ) from e
+
        return None, None


@@ -68,8 +68,12 @@ class MultiProcessEvolvingStrategy(EvolvingStrategy):
        # if the number of factors to be implemented is larger than the limit, we need to select some of them
        if FACTOR_IMPLEMENT_SETTINGS.select_ratio < 1:
            # if the number of loops is equal to the select_loop, we need to select some of them
-            implementation_factors_per_round = round(FACTOR_IMPLEMENT_SETTINGS.select_ratio * len(to_be_finished_task_index) + 0.5)  # ceilling
-            implementation_factors_per_round = min(implementation_factors_per_round, len(to_be_finished_task_index))  # but not exceed the total number of tasks
+            implementation_factors_per_round = round(
+                FACTOR_IMPLEMENT_SETTINGS.select_ratio * len(to_be_finished_task_index) + 0.5
+            )  # ceilling
+            implementation_factors_per_round = min(
+                implementation_factors_per_round, len(to_be_finished_task_index)
+            )  # but not exceed the total number of tasks

            if FACTOR_IMPLEMENT_SETTINGS.select_method == "random":
                to_be_finished_task_index = RandomSelect(
@@ -10,11 +10,7 @@ import pandas as pd
 from filelock import FileLock

 from rdagent.components.coder.factor_coder.config import FACTOR_IMPLEMENT_SETTINGS
-from rdagent.core.exception import (
-    CodeFormatError,
-    NoOutputError,
-    CustomRuntimeError,
-)
+from rdagent.core.exception import CodeFormatError, CustomRuntimeError, NoOutputError
 from rdagent.core.experiment import Experiment, FBWorkspace, Task
 from rdagent.log import rdagent_logger as logger
 from rdagent.oai.llm_utils import md5_hash
@@ -16,7 +16,14 @@ from rdagent.utils import get_module_by_module_path

 class ModelTask(Task):
    def __init__(
-        self, name: str, description: str, formulation: str, architecture: str, variables: Dict[str, str], hyperparameters: Dict[str, str], model_type: Optional[str] = None
+        self,
+        name: str,
+        description: str,
+        formulation: str,
+        architecture: str,
+        variables: Dict[str, str],
+        hyperparameters: Dict[str, str],
+        model_type: Optional[str] = None,
    ) -> None:
        self.name: str = name
        self.description: str = description
@@ -24,7 +31,9 @@ class ModelTask(Task):
        self.architecture: str = architecture
        self.variables: str = variables
        self.hyperparameters: str = hyperparameters
-        self.model_type: str = model_type  # Tabular for tabular model, TimesSeries for time series model, Graph for graph model 
+        self.model_type: str = (
+            model_type  # Tabular for tabular model, TimesSeries for time series model, Graph for graph model
+        )

    def get_task_information(self):
        return f"""name: {self.name}
@@ -107,21 +116,21 @@ class ModelFBWorkspace(FBWorkspace):
            # Initialize all parameters of `m` to `param_init_value`
            for _, param in m.named_parameters():
                param.data.fill_(param_init_value)
-            
+
            # Execute the model
            if self.target_task.model_type == "Graph":
                out = m(*data)
            else:
                out = m(data)
-            
+
            execution_model_output = out.cpu().detach()
            execution_feedback_str = f"Execution successful, output tensor shape: {execution_model_output.shape}"
-            
+
            if MODEL_IMPL_SETTINGS.enable_execution_cache:
                pickle.dump((execution_feedback_str, execution_model_output), open(cache_file_path, "wb"))
-            
+
            return execution_feedback_str, execution_model_output
-        
+
        except Exception as e:
            return f"Execution error: {e}", None